Cloud computing has evolved far beyond a cost-saving measure. Over the past several years it has become the primary platform for enterprise digital transformation, underpinning emerging technologies such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT). Understanding the trends driving cloud adoption helps infrastructure and DevOps teams plan architecture decisions, budget cycles, and skills development with greater confidence.
The seven trends below were identified as major forces reshaping enterprise computing. While the original analysis was published in early 2020, most of these shifts have only accelerated and remain highly relevant to engineering teams today.
1. The Continued Rise of Hybrid Cloud
Enterprises rarely commit to a single cloud provider. Instead, they distribute workloads across multiple public clouds and on-premises infrastructure — a model known as hybrid or multi-cloud. Research firm Gartner predicted that by 2020, 75 percent of organizations would have deployed a multi-cloud or hybrid cloud model, driven by the need for flexibility, vendor independence, and regulatory compliance.
MarketsandMarkets projected the hybrid cloud market would grow from approximately $44.6 billion in 2018 to $97.6 billion by 2023, at a compound annual growth rate (CAGR) of around 17 percent. Key drivers include cost efficiency, scalability, operational agility, and the ability to keep sensitive workloads on-premises while shifting less critical services to public cloud providers.
For DevOps teams, hybrid cloud demands investment in unified management tooling, consistent identity and access controls across providers, and portable CI/CD pipelines that deploy reliably to any target environment.
2. Edge Computing Moves to Center Stage
The proliferation of IoT devices is generating data volumes that centralized cloud data centers struggle to absorb efficiently. Gartner forecast that 75 percent of enterprise-generated data would be created and processed outside a traditional centralized data center or cloud by 2025. An IDC FutureScape report projected that by 2022, 40 percent of enterprises would have doubled their IT asset spending in edge locations compared with core data centers.
Edge computing addresses this by moving compute and storage resources closer to where data is generated — factory floors, retail locations, vehicles, and remote facilities. Lower latency, reduced bandwidth costs, and improved resilience when connectivity to a central cloud is intermittent are the primary benefits.
For engineering teams, edge deployments introduce new operational challenges: managing a large fleet of distributed nodes, ensuring consistent software delivery, and maintaining security across potentially thousands of edge sites. Kubernetes distributions designed for resource-constrained environments (such as K3s) have emerged as a practical response to these challenges.
3. Hyperscale Data Centers Scale Up
Hyperscale refers to an IT architecture’s ability to scale compute, storage, and networking resources rapidly and proportionally in response to demand. Hyperscale data centers — operated by the largest cloud providers and technology companies — are designed from the ground up for elastic capacity.
MarketsandMarkets estimated the hyperscale data center market would grow from approximately $25 billion in 2017 to over $80 billion by 2022. Cisco predicted that by 2021, traffic within hyperscale data centers would quadruple and that hyperscale facilities would account for 55 percent of all data center traffic.
The modular design of hyperscale infrastructure means that individual components — storage nodes, compute blades, and network switches — can be added or replaced independently, giving operators precise control over capacity without large-scale forklift upgrades.
4. Disaster Recovery as a Service (DRaaS) Gains Momentum
Unplanned downtime is expensive. Gartner estimated the average cost of IT downtime at approximately $5,600 per minute, while IBM’s Cost of a Data Breach report put the average total cost of a breach at close to $3.92 million. Regulatory frameworks such as GDPR add a compliance dimension: organizations must demonstrate that they can recover systems and data within defined timeframes.
Disaster Recovery as a Service (DRaaS) addresses this by providing automated, cloud-based failover and recovery capabilities. Rather than maintaining expensive secondary data centers, organizations replicate workloads to a cloud provider and invoke recovery runbooks automatically. IDC projected the DRaaS market would reach $4.5 billion by 2020, growing at approximately 15 percent annually through 2023.
For DevOps practitioners, DRaaS fits naturally into infrastructure-as-code workflows: recovery environments can be defined declaratively, tested in isolation using chaos engineering practices, and spun up on demand rather than maintained continuously at full cost.
5. Hyperconverged Infrastructure (HCI) Sees Increased Adoption
Hyperconverged infrastructure (HCI) integrates compute, storage, and networking into a single software-defined platform managed through a unified interface. This approach eliminates much of the complexity associated with traditional three-tier data center architectures and reduces the operational burden on smaller IT teams.
Gartner predicted that by 2020, 20 percent of business-critical applications then running on three-tier infrastructure would migrate to HCI. Research firm Mordor Intelligence projected the HCI market would register a CAGR of over 13 percent from 2019 to 2024, driven by its simpler management model and lower total cost of ownership compared with disaggregated legacy infrastructure.
HCI is increasingly positioned as an on-premises alternative or complement to public cloud platforms, particularly for organizations with data sovereignty requirements or latency-sensitive workloads that cannot easily move off-premises.
6. AI Transforms Data Center Operations
Artificial intelligence is becoming an operational tool within data centers themselves, not only in the applications they host. AI-based systems can analyze historical telemetry to predict hardware failures before they occur, automatically rebalance workloads to optimize energy consumption, and flag anomalous network behavior that may indicate a security incident.
Gartner predicted that by 2020, 75 percent of organizations would experience visible business disruptions due to infrastructure and operations skill gaps — a significant increase from fewer than 20 percent in 2016. AI-driven automation is one response to this skills shortage, handling routine operational tasks and escalating only genuinely novel problems to human engineers.
Gartner also warned that data centers failing to prepare adequately for AI workloads risked becoming operationally or economically unviable. Modern GPU-dense infrastructure, high-bandwidth networking, and low-latency storage are prerequisites for serving AI inference and training at scale — requirements that differ substantially from traditional CPU-centric designs.
7. Containers Become the Standard Deployment Unit
Containers package application code together with its dependencies, making applications portable across development laptops, on-premises servers, and any public cloud. This “build once, run anywhere” property has made containers the default packaging format for microservices-based architectures.
Gartner predicted that by 2023, more than 70 percent of global organizations would be running more than two containerized applications in production, up from fewer than 20 percent in 2019. IDC projected that 95 percent of new microservices would be deployed in containers by 2021.
Container adoption is closely coupled with hybrid cloud growth: because containers abstract away underlying infrastructure differences, they simplify deployment across heterogeneous environments. Kubernetes has emerged as the dominant container orchestration platform, supported by all major cloud providers and a large ecosystem of tooling covering networking, storage, observability, and policy enforcement.
What These Trends Mean for Engineering Teams
These seven trends reinforce a consistent theme: enterprise IT infrastructure is becoming more distributed, more automated, and more software-defined. The boundaries between on-premises and cloud, between development and operations, and between application code and infrastructure configuration continue to blur.
- Platform engineers should invest in tooling that works consistently across hybrid and multi-cloud environments.
- Infrastructure teams should evaluate HCI and edge deployment models for workloads with latency or data-sovereignty constraints.
- SRE and operations teams should formalize DRaaS strategies and validate them regularly with automated failover tests.
- Developers should treat containers as the default packaging format and design services for portability from the outset.
- Leadership should plan for AI-driven automation to absorb routine operational tasks as the skills gap widens.
Cloud computing is not a destination but an ongoing architectural evolution. Teams that track these trends and build adaptable infrastructure will be better positioned to deliver reliable, scalable services regardless of how the technology landscape continues to shift.
Original analysis sourced from entrepreneur.com (2020). Market figures reflect analyst predictions available at the time of original publication.